Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

Gulzar, Yonis

doi:10.3390/su15031906

Cited by 157 publications

(91 citation statements)

References 36 publications

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“…In addition to these two categories, there are many other research approaches aimed at addressing few-shot learning problems. TL-MobileNetV2 [25] is a deep transfer learning model based on MobileNetV2 for fruit image classification. The model initializes the MobileNetV2 model with a pre-trained model on the ImageNet dataset and then fine-tunes it on a fruit image dataset to learn the discriminative features of fruit images.…”

Section: Related Workmentioning

confidence: 99%

TMNIO:Triplet merged network with involution operators for improved few‐shot image classification

Lulu,

Ranhui,

Shaojie

et al. 2024

IET Image Processing

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Few‐shot learning enables machines to learn efficiently from limited labelled data. However, existing few‐shot learning methods may perform poorly when there is a lack of sufficient samples, and may encounter problems such as domain shift or overfitting when applied to new domains or tasks. To address the issues of poor fitting and insufficient generalization ability in new domains, a new method called triplet merged network with involution operators (TMNIO) is proposed. This method employs dual encoders that extract common and distinctive features from the prototype network, thereby enhancing the model's feature extraction capability. To further improve this ability, the traditional convolutional kernels are replaced with involution operators, which not only reduce the parameter count but also enlarge the receptive field to better extract local feature information. Additionally, this method employs a two‐stage training strategy, where triplet loss is used in the first stage to train the model and enhance its robustness and generalization ability. Extensive experiments on the miniImageNet, Omniglot, and Caltech‐UCSD Birds‐200 (CUB) datasets have shown that our proposed method achieved significant improvements in both training speed and accuracy, particularly on the miniImageNet dataset, where it achieved an outstanding 10% performance improvement.

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Section: Related Workmentioning

confidence: 99%

TMNIO:Triplet merged network with involution operators for improved few‐shot image classification

Lulu,

Ranhui,

Shaojie

et al. 2024

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Moreover, the traditional data augmentation techniques [33][34][35], such as oversampling and undersampling, remove considerable amount of data belonging to the majority class, which results in the loss of its important features. The conventional techniques can generate only specific types of distributions.…”

Section: Motivationmentioning

confidence: 99%

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

et al. 2023

Self Cite

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Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets.

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“…So, Improvement methods for MobileNet network structure in the field of image classification have been successively proposed in recent years. For example, in 2019, Yonis Gulzar [24] designed a specific five-layer in mobilenetv2 network while retaining the pre-trained model using migration learning, and achieved good results in automatically extracting fruit features for recognition. Yue Pang [25] designed an improved mobilenetv2 network in order to solve the problem of sheep recognition and tracking in large-scale sheep farming, while a series of validation tests were carried out.…”

Section: Introductionmentioning

confidence: 99%

A Study on Expression Recognition Based on Improved MobileNetV2 Network

Zhu,

Zhuang,

Zhao

et al. 2024

Preprint

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This paper proposes an improved strategy for the MobileNetV2 neural network(I-MobileNetV2) in response to problems such as large parameter quantities in existing deep convolutional neural networks and the shortcomings of the lightweight neural network MobileNetV2 such as easy loss of feature information, poor real-time performance, and low accuracy rate in facial emotion recognition tasks. The network inherits the characteristics of MobilenetV2 depthwise separated convolution, signifying a reduction in computational load while maintaining a lightweight profile. It utilizes a reverse fusion mechanism to retain negative features, which makes the information less likely to be lost. The SELU activation function is used to replace the RELU6 activation function to avoid gradient vanishing. Meanwhile, to improve the feature recognition capability, the channel attention mechanism (Squeeze-and-Excitation Networks (SE-Net)) is integrated into the MobilenetV2 network. Experiments conducted on the facial expression datasets FER2013 and CK + showed that the proposed network model achieved facial expression recognition accuracies of 68.62% and 95.96%, improving upon the MobileNetV2 model by 0.72% and 6.14% respectively, and the parameter count decreased by 83.8%. These results empirically verify the effectiveness of the improvements made to the network model.

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Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

Cited by 157 publications

References 36 publications

TMNIO:Triplet merged network with involution operators for improved few‐shot image classification

TMNIO:Triplet merged network with involution operators for improved few‐shot image classification

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

A Study on Expression Recognition Based on Improved MobileNetV2 Network

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